Geophysical Reservoir Monitoring GRM systems such 4D seismic are increasingly used in the oil and gas industry because they provide unique and useful information on fluid movement within the reservoir. This information is relevant for many reservoir management decisions; including new well placement, well intervention, and reservoir model updating.
Unfortunately, it has been difficult to estimate the value creation of any data acquisition scheme due to the fact that a multidisciplinary approach is required to model the value that future measurements will imply in future decisions. This assessment requires a common decision making simulation frame work that can integrate the input from geo-modelers, geophysicist and reservoir engineers.
This work presents an example of how a Close Loop Reservoir Management (CLRM) simplification can be used as a framework for simulating NPV changes due to assimilation of production and saturations in a simple toy model. It combines state-of-the-art data assimilation and uncertainty modeling methods with a robust optimization genetic algorithm to calculate NPV improvements due to model update and its relationship with the NPV obtained from the synthetic reservoir.
In this context a simple synthetic model is presented. It recreates a segment of green field under a strong aquifer influence with two discovery wells. The reservoir development requires the selection of 4 well locations at fixed drilling times. The development strategy selection is obtained with the use of a genetic algorithm within the CLRM framework. Subsequently two cases are presented: one of assimilating only production after the first two wells have been drilled, just before deciding the locations of the last two wells; and a second case, in which production and saturation are assimilated at the same time. The saturation map assimilated is assumed to be output of a 4D seismic acquisition. The model update imposes the need of optimally relocate the last two wells which results in a NPV change.
The results show how the obtained NPVs is incremented by the relocation of the last two wells in both cases. A bigger increment is obtained when both, production and saturation are assimilated. In addition, the ensemble improved its forecast capability the most, when saturation assimilation is included. Nevertheless, the ensemble expected NPV decreases after assimilation from the value obtained from the first development strategy optimization; this indicates an optimistic early NPV valuation due to the initial ensemble distributions spread.
The study presents an asset simulation framework that could be used to evaluate data acquisition investments through the systematic modeling of reservoir uncertainties with in a decision oriented focus. This could include the inclusion of additional uncertain model parameters, the insertion of water injector and well conversions, the assimilation of saturations at different intervals, the change on the quality of the saturation maps assimilated, in addition to sensitivity studies of other economic constrains.